public Instances transformInstances(MultiLabelInstances mlData) throws Exception {
labelIndices = mlData.getLabelIndices();
numOfLabels = mlData.getNumLabels();
Instances data = mlData.getDataSet();
Instances transformed = new Instances(mlData.getDataSet(), 0);
// delete all labels
transformed = RemoveAllLabels.transformInstances(transformed, labelIndices);
// add single label attribute
ArrayList<String> classValues = new ArrayList<String>(numOfLabels);
for (int x = 0; x < numOfLabels; x++) {
classValues.add("Class" + (x + 1));
}
Attribute newClass = new Attribute("Class", classValues);
transformed.insertAttributeAt(newClass, transformed.numAttributes());
transformed.setClassIndex(transformed.numAttributes() - 1);
for (int instanceIndex = 0; instanceIndex < data.numInstances(); instanceIndex++) {
// System.out.println(data.instance(instanceIndex).toString());
List<Instance> result = transformInstance(data.instance(instanceIndex));
for (Instance instance : result) {
// System.out.println(instance.toString());
transformed.add(instance);
// System.out.println(transformed.instance(transformed.numInstances()-1));
}
}
return transformed;
}
/**
* Calculate metric value
*
* @param mlData Multi-label dataset to which calculate the metric
* @return Value of the metric
*/
public double calculate(MultiLabelInstances mlData) {
Instances instances = mlData.getDataSet();
int nInstances = mlData.getNumInstances();
double avg;
double var2;
double var4;
double val;
int nNumeric = 0;
double mean = 0;
Set<Attribute> attributesSet = mlData.getFeatureAttributes();
for (Attribute att : attributesSet) {
if (att.isNumeric()) {
nNumeric++;
avg = instances.meanOrMode(att);
var2 = 0;
var4 = 0;
for (Instance inst : instances) {
val = inst.value(att);
var2 += Math.pow(val - avg, 2);
var4 += Math.pow(val - avg, 4);
}
double kurtosis = (nInstances * var4 / Math.pow(var2, 2)) - 3;
double sampleKurtosis =
(kurtosis * (nInstances + 1) + 6)
* (nInstances - 1)
/ ((nInstances - 2) * (nInstances - 3));
mean += sampleKurtosis;
}
}
if (nNumeric > 0) {
mean = mean / nNumeric;
} else {
mean = Double.NaN;
}
this.value = mean;
return value;
}
@Override
protected void buildInternal(MultiLabelInstances trainingSet) throws Exception {
header = new Instances(trainingSet.getDataSet(), 0);
baseLearner.build(trainingSet);
}
